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Lisheng Wang

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

DCTR: Dual-Constraint Subgraph Optimization for Knowledge Graph-based Retrieval-Augmented Generation

Yukun Cao
Zirui Xu
Dongyang Li
Zhihao Guo
Luobin Huang
LIsheng Wang

Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) shifts the contents of retrieval from narrative text to a relational knowledge network, empowering large language models (LLMs) to harness structured relationships between entities. However, conventional KG-RAG approaches are resource-intensive, requiring either query decomposition with multiple LLM rounds or parameterized static knowledge injection to update the model. Although subgraph reasoning aims to address these issues, most current methods are based on heuristic shortest path and multi-hop graph traversal algorithms. The retrieved subgraphs suffer from incompleteness and semantic drift, and neglect the interaction between subgraph and LLMs in terms of fine-grained structural semantics. We propose a dual-constraint subgraph optimization for KG-RAG (DCTR). It improves subgraph retrieval and generates high-quality subgraphs with structural integrity and information salience for LLMs. Specifically, it formulates subgraph generation as a two-stage graph-theoretic constrained optimization problem to create compact and complete pseudo-labels. Since these pseudo-labels are discrete, a smooth approximation is employed to convert them into a differentiable representation, thereby optimizing the retriever to highlight key information while extracting subgraphs. On two benchmark datasets, DCTR significantly enhances subgraph quality, achieving state-of-the-art performance in LLM reasoning.

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JBHI Journal 2026 Journal Article

Head-and-Neck Organs Segmentation in CT Based on Spatial Prior and Shape Description

Chengyang An
Tao Yang
Xiao Sun
Yu Qiao
Yubing Li
Jilan Jiang
Ling Zhu
LIsheng Wang

Accurate delineation of organs at risk (OARs) is critical for effective radiotherapy in head and neck cancer, and different deep learning methods have been proposed for this task. Although these methods can effectively segment large organs, they all face challenges in segmenting different small organs with high accuracy, due to large numbers, complex distributions, and diverse shapes of small organs in the head and neck region. To address this challenge, this paper proposes a novel segmentation framework that incorporates spatial distribution information of all organs and shape priors of small organs into deep networks to constrain and enhance small organ segmentation. First, a spatial guidance network (SG-Net) is proposed to generate spatial guidance maps (SGMs) of organs, emphasizing the boundaries of different organs and their spatial positional relationships, thereby providing useful spatial cues to constrain organ segmentation. Second, for small-volume organs, we specifically design a deep shape description module (DSDM) to extract organ-specific shape features from CT images and integrate them into the original deep features to enhance the features' sensitivity to shape constraints. Finally, a regularization term is employed to reduce excessive smoothing in the predicted probability maps of the deep network, preserving the shape details of small organs. With this framework, while the segmentation accuracy of large organs is maintained, small organ segmentation is significantly improved. Experimental results demonstrate its effectiveness for segmentation of small organs, with a significant improvement over state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

Mnemosyne: Accelerating Multi-Hop Question Answering via Cache Hit Order Fitting

Haizhou Du
Jiujiu Li
Dongyang Li
Luobin Huang
LIsheng Wang

Multi-Hop Question Answering (MHQA) requires step-by-step reasoning across multiple pieces of information to answer complex questions. The cache-aided Retrieval-Augmented Generation (RAG) can accelerate the process of external knowledge retrieval at each reasoning step for MHQA. However, existing methods focus on the internal structure and ignore the misalignment between the queries’ arrival order and cache hit order. To tackle this, we propose Mnemosyne, a cache hit order fitting method designed to accelerate the RAG progress for MHQA. Specifically, our cache-aware order fitting strategy adjusts the order of queries arrival via graph reordering to better align with the cache hit order, thereby reducing the likelihood of failed or unproductive retrieval attempts. The multi-granularity caching storage mechanism is designed to loosen the strict hit condition to multiple similar semantic matching modes, facilitating that relevant documents can still be retrieved. Experiments conducted on four multi-hop QA datasets demonstrate that Mnemosyne effectively reduces retrieval latency while enhancing task answer F1 score, achieving a superior trade-off between efficiency and effectiveness.

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JBHI Journal 2026 Journal Article

Morphology Prior Enhanced Teeth Segmentation for High-Resolution Oral Scans

Yuxian Jiang
Xiuying Wang
Tao Yang
Changkai Ji
Lanshan He
Yusheng Liu
Wei Wang
Min Liu

Deep learning methods have been proposed for tooth segmentation on high-resolution intra-oral scans (IOS) that plays a crucial role in clinical dental practice. However, they generally segment teeth in a low-resolution data with a fixed receptive field and generate final segmentation by up-sampling interpolation, and neglect teeth’s morphology priors: their similar dental arch structures and significantly different curvatures in different parts of each tooth. They thus lack adaptability to different parts of each tooth, and show less accurate segmentation of boundary points between teeth and gums due to the up-sampling computation. Further, cluttered poses of IOS limit their generalization and usability of teeth location and geometric information. To address these limitations, a morphology prior enhanced teeth segmentation framework is proposed in this paper. Firstly, a robust preprocessing is introduced to align poses of different IOS by computing their dental arch orientations, thereby improving segmentation generalization and usability of IOS geometric information. Secondly, a decomposition-merging strategy is designed to avoid the up-sampling limitation, which decomposes an IOS into multiple low-resolution data and merges their segmentation outcomes into a high-resolution result. Thirdly, an innovative module integrating semantic and geometric features is proposed to adaptively select deformable receptive fields. It geometrically samples within a variable probability space to construct receptive fields with varied graph relationships for different points, facilitating adaptive segmentation of different parts of each tooth. Experimental results on 6238 IOS from four centers demonstrate that our method significantly outperforms 11 state-of-the-art methods, achieving a 6. 93% enhancement for cross-center testing.

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AAAI Conference 2025 Conference Paper

DPCL-Diff：Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning

Yukun Cao
LIsheng Wang
Luobin Huang

Temporal knowledge graph (TKG) reasoning that infers future missing facts is an essential and challenging task. Predicting future events typically relies on closely related historical facts, yielding more accurate results for repetitive or periodic events. However, for future events with sparse historical interactions, the effectiveness of this method, which focuses on leveraging high-frequency historical information, diminishes. Recently, the capabilities of diffusion models in image generation have opened new opportunities for TKG reasoning. Therefore, we propose a graph node diffusion model with dual-domain periodic contrastive learning (DPCL-Diff). Graph node diffusion model (GNDiff) introduces noise into sparsely related events to simulate new events, generating high-quality data that better conforms to the actual distribution. This generative mechanism significantly enhances the model's ability to reason about new events. Additionally, the dual-domain periodic contrastive learning (DPCL) maps periodic and non-periodic event entities to Poincaré and Euclidean spaces, leveraging their characteristics to distinguish similar periodic events effectively. Experimental results on four public datasets demonstrate that DPCL-Diff significantly outperforms state-of-the-art TKG models in event prediction, demonstrating our approach's effectiveness. This study also investigates the combined effectiveness of GNDiff and DPCL in TKG tasks.

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ECAI Conference 2025 Conference Paper

Reinforced and Integrated Prompt Optimization Strategy for Emotion Recognition in Conversation

Yukun Cao
Luobin Huang
Zhihao Guo
Lisheng Wang

Emotion recognition in conversation (ERC) aims to identify the emotion expressed in each utterance within a multi-turn dialogue. In recent years, the widespread adoption of language models (LM) has spurred the development of various prompting paradigms as effective adaptation strategies, aligning LM training objectives with the specific requirements of ERC. However, while the hard prompt-based paradigm offers high human interpretability, it suffers from limited task adaptability due to its discrete nature. In contrast, the soft prompt-based paradigm sacrifices interpretability in favor of improved adaptability by optimizing continuous embedding vectors. Both paradigms generally adopt an invariant prompt across utterances, which restricts their ability to model contextual diversity and results in suboptimal adaptability of the instance. To address these issues, we propose a comprehensive prompting strategy that balances interpretability and adaptability, consisting of two components: reinforced prompt exploration for hard prompts and feature integration for soft prompts. In reinforced prompt exploration, a policy network is trained via reinforcement learning to explore the discrete prompt space under cold-start conditions, efficiently optimizing hard prompts and improving task-level adaptability while preserving interpretability. In feature integration for soft prompts, we incorporate rich semantic features to form contextually relevant soft prompts, assigning each utterance a distinct offset subspace to improve instance-level adaptability. Experiments on three datasets demonstrate that our prompting strategy achieves state-of-the-art performance in ERC.

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JBHI Journal 2024 Journal Article

3D Vessel Segmentation With Limited Guidance of 2D Structure-Agnostic Vessel Annotations

Huai Chen
Xiuying Wang
Hui Li
LIsheng Wang

Delineating 3D blood vessels of various anatomical structures is essential for clinical diagnosis and treatment, however, is challenging due to complex structure variations and varied imaging conditions. Although recent supervised deep learning models have demonstrated their superior capacity in automatic 3D vessel segmentation, the reliance on expensive 3D manual annotations and limited capacity for annotation reuse among different vascular structures hinder their clinical applications. To avoid the repetitive and costly annotating process for each vascular structure and make full use of existing annotations, this paper proposes a novel 3D shape-guided local discrimination (3D-SLD) model for 3D vascular segmentation under limited guidance from public 2D vessel annotations. The primary hypothesis is that 3D vessels are composed of semantically similar voxels and often exhibit tree-shaped morphology. Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images. Secondly, the shape distribution from existing 2D structure-agnostic vessel annotations is introduced to guide the 3D vessels with the tree-shaped morphology by the adversarial shape constraint loss. Thirdly, to enhance training stability and prediction credibility, the highlighting-reviewing-summarizing (HRS) mechanism is proposed. This mechanism involves summarizing historical models to maintain temporal consistency and identifying credible pseudo labels as reliable supervision signals. Only guided by public 2D coronary artery annotations, our method achieves results comparable to SOTA barely-supervised methods in 3D cerebrovascular segmentation, and the best DSC in 3D hepatic vessel segmentation, demonstrating the effectiveness of our method.

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JBHI Journal 2022 Journal Article

3D Graph-Connectivity Constrained Network for Hepatic Vessel Segmentation

Ruikun Li
Yi-Jie Huang
Huai Chen
Xiaoqing Liu
Yizhou Yu
Dahong Qian
LIsheng Wang

Segmentation of hepatic vessels from 3D CT images is necessary for accurate diagnosis and preoperative planning for liver cancer. However, due to the low contrast and high noises of CT images, automatic hepatic vessel segmentation is a challenging task. Hepatic vessels are connected branches containing thick and thin blood vessels, showing an important structural characteristic or a prior: the connectivity of blood vessels. However, this is rarely applied in existing methods. In this paper, we segment hepatic vessels from 3D CT images by utilizing the connectivity prior. To this end, a graph neural network (GNN) used to describe the connectivity prior of hepatic vessels is integrated into a general convolutional neural network (CNN). Specifically, a graph attention network (GAT) is first used to model the graphical connectivity information of hepatic vessels, which can be trained with the vascular connectivity graph constructed directly from the ground truths. Second, the GAT is integrated with a lightweight 3D U-Net by an efficient mechanism called the plug-in mode, in which the GAT is incorporated into the U-Net as a multi-task branch and is only used to supervise the training procedure of the U-Net with the connectivity prior. The GAT will not be used in the inference stage, and thus will not increase the hardware and time costs of the inference stage compared with the U-Net. Therefore, hepatic vessel segmentation can be well improved in an efficient mode. Extensive experiments on two public datasets show that the proposed method is superior to related works in accuracy and connectivity of hepatic vessel segmentation.

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JBHI Journal 2022 Journal Article

Building a Risk Prediction Model for Postoperative Pulmonary Vein Obstruction via Quantitative Analysis of CTA Images

Yuchen Pei
Guocheng Shi
Wenjin Xia
Chen Wen
Dazhen Sun
Fang Zhu
Jiang Li
Zhongqun Zhu

Total anomalous pulmonary venous connection (TAPVC) is a rare but mortal congenital heart disease in children and can be repaired by surgical operations. However, some patients may suffer from pulmonary venous obstruction (PVO) after surgery with insufficient blood supply, necessitating special follow-up strategy and treatment. Therefore, it is a clinically important yet challenging problem to predict such patients before surgery. In this paper, we address this issue and propose a computational framework to determine the risk factors for postoperative PVO (PPVO) from computed tomography angiography (CTA) images and build the PPVO risk prediction model. From clinical experiences, such risk factors are likely from the left atrium (LA) and pulmonary vein (PV) of the patient. Thus, 3D models of LA and PV are first reconstructed from low-dose CTA images. Then, a feature pool is built by computing different morphological features from 3D models of LA and PV, and the coupling spatial features of LA and PV. Finally, four risk factors are identified from the feature pool using the machine learning techniques, followed by a risk prediction model. As a result, not only PPVO patients can be effectively predicted but also qualitative risk factors reported in the literature can now be quantified. Finally, the risk prediction model is evaluated on two independent clinical datasets from two hospitals. The model can achieve the AUC values of 0. 88 and 0. 87 respectively, demonstrating its effectiveness in risk prediction.

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